Probabilistic localization with a blind robot

Abstract

Researchers have addressed the localization problem for mobile robots using many different kinds of sensors, including rangefinders, cameras, and odometers. In this paper, we consider localization using a robot that is virtually "blind", having only a clock and contact sensor at its disposal. This represents a drastic reduction in sensing requirements, even in light of existing work that considers localization with limited sensing. We present probabilistic techniques that represent and update the robot's position uncertainty and algorithms to reduce this uncertainty. We demonstrate the experimental effectiveness of these methods using a Roomba autonomous vacuum cleaner robot in laboratory environments.